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TAT-HUM: Trajectory analysis toolkit for human movements in Python

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Abstract

Human movement trajectories can reveal useful insights regarding the underlying mechanisms of human behaviors. Extracting information from movement trajectories, however, can be challenging because of their complex and dynamic nature. The current paper presents a Python toolkit developed to help users analyze and extract meaningful information from the trajectories of discrete rapid aiming movements executed by humans. This toolkit uses various open-source Python libraries, such as NumPy and SciPy, and offers a collection of common functionalities to analyze movement trajectory data. To ensure flexibility and ease of use, the toolkit offers two approaches: an automated approach that processes raw data and generates relevant measures automatically, and a manual approach that allows users to selectively use different functions based on their specific needs. A behavioral experiment based on the spatial cueing paradigm was conducted to illustrate how one can use this toolkit in practice. Readers are encouraged to access the publicly available data and relevant analysis scripts as an opportunity to learn about kinematic analysis for human movements.

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Open Practices Statement

The data for the experiment in this study are freely available on the Open Science Framework at https://doi.org/10.17605/OSF.IO/24QVM. The corresponding code associated with this toolkit and the analysis script for the accompanying experiment can be found on GitHub at https://github.com/xywang01/TAT-HUM. The experiment reported here was not preregistered.

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Acknowledgments

We would like to thank Craig Chapman and an anonymous reviewer for their constructive feedback that helped to expand the functionalities of this toolkit. We would also like the thank members of the Centre for Motor Control in the Faculty of Kinesiology and Physical Education at the University of Toronto for their assistance that has made this project possible. Finally, we would also like to thank Damian Manzone, Luc Tremblay, and Bev Larssen for providing sample 2D movement data as demos.

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  1. Centre for Motor Control, Faculty of Kinesiology & Physical Education, University of Toronto, Toronto, Ontario, Canada

    Xiaoye Michael Wang & Timothy N. Welsh

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  1. Xiaoye Michael Wang
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Correspondence to Xiaoye Michael Wang.

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Wang, X.M., Welsh, T.N. TAT-HUM: Trajectory analysis toolkit for human movements in Python. Behav Res (2024). https://doi.org/10.3758/s13428-024-02378-4

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